Reinforcement Learning in Mental Health: A Comprehensive Review

Reinforcement learning (RL), a core paradigm within machine learning, has gained increasing attention in mental health research due to its ability to model adaptive decision-making over time. Unlike static predictive approaches, RL frameworks learn from continuous feedback, making them particularly suitable for personalized and dynamic mental health interventions. This review examines the theoretical foundations of reinforcement learning and synthesizes its emerging applications across clinical decision support, digital mental health interventions, and computational psychiatry. It discusses how RL has been used to personalize treatment strategies, model reward and punishment learning in psychiatric disorders, and support adaptive digital therapeutics. Key challenges related to model interpretability, ethical deployment, data availability, and clinical integration are critically analyzed. The review concludes that while reinforcement learning offers substantial promise for advancing personalized mental healthcare, its successful translation into real-world clinical practice will require close collaboration between clinicians, data scientists, and ethicists.